Scalable real-time parking lot classification: an evaluation of image features and supervised learning algorithms

Tschentscher, Marc and Koch, Christian and König, Markus and Salmen, Jan and Schlipsing, Marc (2015) Scalable real-time parking lot classification: an evaluation of image features and supervised learning algorithms. In: 2015 International Joint Conference on Neural Networks (IJCNN), 12-17 July 2015, Killarney, Ireland.

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The time-consuming search for parking lots could be assisted by efficient routing systems. Still, the needed vacancy detection is either very hardware expensive, lacks detail or does not scale well for industrial application. This paper presents a video-based system for cost-effective detection of vacant parking lots, and an extensive evaluation with respect to the system’s transferability to unseen environments. Therefore, different image features and learning algorithms were examined on three independent datasets for an unbiased validation. A feature / classifier combination which solved the given task against the background of a robustly scalable system, which does not require re-training on new parking areas, was found. In addition, the best feature provides high performance on gray value surveillance cameras. The final system reached an accuracy of 92.33% to 99.96%, depending on the parking rows’ distance, using DoG-features and a support vector machine.

Item Type: Conference or Workshop Item (Paper)
Additional Information: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. doi: 10.1109/IJCNN.2015.7280319
Schools/Departments: University of Nottingham UK Campus > Faculty of Engineering > Department of Civil Engineering
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Depositing User: Eprints, Support
Date Deposited: 25 Jul 2016 13:24
Last Modified: 14 Sep 2016 02:56

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